I native leaderThe Fast Future Fundamentals is a one-of-its-kind course designed to help professionals and leaders develop a solid understanding of the fast future as it develops and get hands-on in building their AI Power Habits.
This is taught Live! and not a self-paced, eLearning program
This is the fastest, simplest and most comprehensive course
available anywhere to come up to speed with our fast future.
Learn Live!
Leaders learn how 12 interconnected, multi-disciplinary topics are shaping 90% of industries, markets, businesses, products & services, and work & leadership, in interactive live sessions with global thought leaders.
125 questions across 25 sub-dimensions show each leader where they stand, then hand them a practice guide of examples, models and prompts for using AI on each of the five skills.
Leaders who see further, decide faster and become proficient in AI Power Habits.
Changed the way I see things, not everything was familiar but made me realize that there is so much outside of my usual work.
Five questions every leadership team should be able to answer in the Age of AI, one for each of the Fast Future Five, and what the course puts in front of your leaders for each.
AI has made answers cheap. The scarce input is now the question worth asking and the idea worth backing, and both come from curiosity. Most leaders only look inside their own industry, which is where change arrives last. The course takes them across twelve topics and industries they have never worked in, so they spot signals three to five years out and bring the idea home.
Speed is an outcome of comprehension. A leader who does not understand a shift cannot sponsor it, fund it or stop it, and transformations most often stall at that sponsor layer. Live sessions give leaders a working grasp of the twelve shifts, from quantum and robotics to the future of money, energy and work, and of how they connect.
AI multiplies the volume of analysis on every desk. Clarity is comprehension made multi-dimensional: seeing the pattern, saying it simply, drawing it on one page and having the courage to say no. The self-assessment measures five clarity habits, from briefing AI precisely to rejecting output that reads well but does not hold.
The value of AI is released by redesign: of the customer experience, of who does which task, of how company knowledge is organised and of what happens when the system gets it wrong. Topics such as Reinventing Work for AI and Robotics & Physical AI show what redesign looks like elsewhere, and the functional guides translate it into each leader's own function.
Execution is the premium skill that turns the other four into results, and it is where most AI spend stalls: between pilot and production, and between activity and measurable value. The AI Power Habits Self-Assessment gives each leader a personal practice plan, and gives you a cohort baseline: which five capabilities to fix first, where the gaps sit by function, and whether the top team is ahead of or behind the people it is asking to change.
A leadership team that sees the shifts early and moves on them together, before change forces its hand.
A five-skill baseline for every leader and for the cohort, cut by function and seniority.
12 live hours over 3 months that bolt on to existing leadership programmes, with guides, onboarding and debriefs included.
Helping students get ready for a poly-career world
Graduates are entering a poly-career world: the role they trained for may not be the role they hold in three years, and the meaning of opportunity has changed under them. The course takes students across twelve topics and industries outside their major, so they arrive at an interview with a view on where value is moving rather than a recitation of the syllabus.
Employers hire for whether a graduate can follow a conversation about the shifts and add to it. Twelve live sessions with the people running those shifts - quantum, robotics, energy, money, mobility, health - give students the vocabulary and the working grasp to use it, which is what turns academic learning into an industry mindset.
After four topics, each student receives the skill guide for their own academic field: ten questions a student in that major must be able to answer, an analytics discussion essay that carries into their assignments, and an applied discussion guide for interviews and project reviews. It is the step from knowing something to saying it clearly under questioning.
Layers have collapsed and technology has pulled the know-how once expected at ten years of experience into the first two. Students who have practised redesigning how work gets done - rather than learning a role as it exists today - graduate able to shape a job rather than wait for one to be defined.
The AI Power Habits Self-Assessment gives every student a baseline across twenty-five sub-dimensions and a practice plan of their own, and gives the department a picture of the cohort: which capabilities to build first, where the gaps sit by field, and whether the cohort is ahead of or behind the employers it is being sent to.
Learning Live! with ability to interact is a valuable human experience. We do not offer recorded content as they get outdated in real time.
We select the 12 Topics that Together Impact 90% of industries, markets, businesses, products & services, and in Work & leadership.
Our faculty are explainers-in-chiefs of the future who are global thought teachers, authors, teach at top Business & technology Schools, associated with WEF Davos Forums, and are Industry Leaders.
Faculty announced shortly
Faculty to be announced.
Dr. Efi Pylarinou
A Wall Street professional turned global fintech and tech thought leader, with a PhD in Finance and a career that began at Salomon Brothers. Founder of Pylarinou Advisory and a top LinkedIn voice with 200,000 followers. Co-author of The Fast Future Blur (Wiley), Theories of Change (Springer) and the WealthTech Wiley book.
Ravin Jesuthasan
Global leader of Mercer’s Transformation Services business and a regular presenter at the World Economic Forum in Davos. Recognised by Thinkers50 and named among the top future-of-work influencers. Author of five books and over 200 articles, including Work Without Jobs, a Wall Street Journal bestseller.
Vince Kuraitis
Thirty years in healthcare as president, VP of corporate development and management consultant, across more than 150 organisations — hospitals, physician groups, medical devices, pharma and health plans. Clients have included Intel Digital Health, Philips, Medtronic and Samsung. MBA and JD from UCLA.
Jessica Groopman
Founder of the Regenerative Technology Project and an industry analyst studying where emerging technology meets regeneration. She has published over 50 reports across culture, business, economics and environment, and is listed among the 100 most influential thought leaders on IoT and Top Women in AI Ethics.
Kateryna Portmann
Senior Product Manager at ANYbotics, a global leader in autonomous robotics, bringing advanced robotic systems into complex industrial environments. Previously Global Product Lead for Connected Care and Data at Hocoma. She co-leads the Swiss chapter of Women in Robotics and speaks on AI governance and responsible automation.
Sagar Kancharla
Nearly 25 years across the energy landscape — inside an energy company, an energy regulator and now a consultancy. He advises organisations on renewable power, hydrogen, carbon capture, renewable natural gas, storage, geothermal and district energy, on projects across Canada, the US, Colombia, Norway, Sweden and New Zealand.
Dr. Alun Evans
CEO and co-founder of Freeverse.io, the home of Living Assets, with fifteen years in technology for entertainment. Previously CEO of Shar3d.io and CTO of Bodypal.com. He holds a PhD in Medical Physics from University College London and lectures in computer graphics at La Salle.
Cortney Harding
Founder of the award-winning agency Friends With Holograms, making VR training for Lowe’s, Walmart, PwC, Amazon and Target. Her work was named Best VR/AR at Mobile World Congress and an SXSW Innovation Award finalist. She is currently working with Meta on learning and training in the metaverse.
Hari Abburi
Managing Partner of The Preparation Company and co-founder of MyLearningNFT, with 30 years of experience across more than 55 countries in M&A and integrations, complex transformations and start-ups. His agility framework sets out strategy through Intersections, Interfaces and Insights.
Jessica Robinson
Co-founder and partner of Assembly Ventures, investing in the mobility companies moving the Western world. She co-founded the Michigan Mobility Institute and led next-generation mobility as Director of City Solutions at Ford Smart Mobility, after helping build Zipcar, the world’s largest car-sharing organisation.
Ramanathan Srikumar
Three decades in senior technology roles in financial services. He heads the Portfolio Group at Mphasis, leading industry specialists, solution and enterprise architects and cloud experts. Previously Regional CIO at Citibank, leading consumer banking technology across Asia.
The five skills that keep you perpetually relevant, measured with AI as the instrument. In about 40 minutes each leader sees where they stand across 25 sub-dimensions, then gets the examples, models and prompts to multiply each skill with AI.
Five questions a company cannot answer about its own leaders today — and what this assessment puts in front of you for each one.
Every company assumes it needs AI training. Most don’t know which kind. The practice by self-report (112 items) helps leaders become power users of AI, driving higher adoption and sharper outcomes. Doing a lot without understanding what it’s doing carries a risk for your AI investments.
The honest answer in most companies is activity — and nobody can prove otherwise. Every question in the instrument sits on a difficulty ladder: applied use, then judgment under complexity, then changed how my team works. Aggregated, that becomes a funnel showing how many leaders make it to each rung. A wide top and an empty bottom is the most common finding, and it is the ROI conversation.
Leaders mark each question for how much it matters to their actual job, so the report plots demand against capability across all twenty-five sub-dimensions. High relevance with low capability is the quadrant that gets funded; low relevance with low capability is the training most companies buy by default. The answer is specific to the company — a manufacturer and a bank do not get the same five. So your company and your leaders have a practice plan that works for you.
The aggregated cohort view shows whether a capability is thin everywhere, uneven between parts of the business, or already established in places — and those three pictures point to three different adoption problems. Thin everywhere is a starting-line problem. Uneven is a spread problem. Established in pockets means the constraint is no longer capability at all, it is how the organisation moves what it already has. As each leader gets a practice guide based on relevance to their job, this becomes a concerted, targeted skills-builder initiative.
Cut by function and by seniority layer. The finding executives react to hardest is when the executive committee scores below the directors it is asking to transform. That changes the sequence of the entire programme, and it is not a conversation anyone can have without evidence.
Twenty-five capabilities is not a plan. The individual report places all twenty-five on one map, names the five that come before the other twenty, and then hands over three things to try for every question that matters.
One question from each sub-dimension — the lowest rung that still carries weight — placed on fixed rules rather than on a curve. The number beside each is the response, 0 to 4. The two knowledge items carry no relevance and sit on the left by right or wrong alone.
Five of the twenty-five come before the rest: without them the rungs above do not hold. Three of these five are in this leader’s learning focus.
Every one of the 112 practice questions carries three actions written for that question — an experiment, a prompt to paste, and a second experiment. The thirteen knowledge questions carry no actions; they are reviewed instead, with the answer and the reason.
Every leader in the cohort has their own map. The cohort report is those maps laid on top of one another — the same four boxes, the same twenty-five, counted.
Each sub-dimension sits in the box where most of the hundred put it, and the count says how many.
Where the hundred fall on each of the five, and how many carry it as their learning focus.
Each bubble is the number of leaders in that function carrying that sub-dimension in Your Learning Focus; the area carries the count, and the colour is the skill it belongs to. Every column adds up to the figure Part A reports for that sub-dimension.
We are in an era of perpetual irrelevance — business models, technology and social change now move faster than people can keep up with them. The response has been a rush toward tangible technical courses, while the underlying skills that let a person adapt go uninvested in, because they are hard to teach and harder to measure.
The Fast Future Five are those underlying skills. Imagination creates economic value far greater than technology itself; companies transform because the curiosity of their people gives them the big idea. Comprehension determines the strategic choices you are able to see — speed is an outcome of comprehension, not of execution. Clarity is comprehension made multi-dimensional, through visual thinking, articulation and the courage to say no. Design, applied beyond products to how a company is actually run, creates three times the value of industry peers. Execution is the premium skill — the one that turns the other four into results.
These skills are learnable. This self-assessment shows you where yours stand when AI is the instrument.
One page per sub-dimension, five questions on each. You can move back and forth freely. Plan to finish in one sitting — your place is kept on this browser if you are interrupted, but not on another device.
Coming shortly.
Twelve interconnected topics shape about 90% of what is coming. That is what the course teaches. This is what to do with it on Monday.
Pick your function. For each of the twelve topics you get three things
Twelve topics · what they mean for this function
Twelve interconnected topics shape about 90% of what is coming. That is what the course teaches. This is what you will be asked about it in the room.
Pick your field of study. For each of the twelve topics you get one question, answered in three moves
Business & Management
Twelve topics · one question each · the twelve you should be able to answer by graduation
Product Management · question 1 of 12
Topic 01 · Foresight, Strategy & Business Models
Product management has long assumed a human user who sees a screen, clicks through a funnel and forms habits. That assumption is weakening. AI assistants can now read documentation, compare offers, fill in forms and complete tasks for a person. When software agents become a meaningful share of your “users,” the product is judged less on how it looks and more on how reliably it delivers an outcome that another system can verify.
When AI makes features cheap to copy, a longer feature list stops being a moat. Durable advantage shifts toward proprietary data that improves the product with use, deep integration into customer workflows, trust earned through reliability, and speed of learning. The product manager’s strategic question changes from “what should we add?” to “what can only we deliver, and how do we get better at it faster than anyone else?”
Products increasingly need two front doors: an interface for people and a clean, well-documented interface for machines, usually an API (a structured way for software to talk to software). Clear pricing, predictable behavior and machine-readable descriptions of what the product does will influence whether an agent recommends you. Think of it as search engine optimization for a world where the shortlist is drawn up by an algorithm.
Seat-based pricing assumes one human per license. If one person directs several agents, or an agent does the work of a team, per-seat revenue can shrink even as usage grows. Expect more usage-based and outcome-based pricing (paying per resolved ticket, per completed task, per result). Product managers will need to model unit economics carefully, because AI features carry real running costs every time they are used, unlike traditional software.
Roadmaps built only on last quarter’s customer requests miss structural change. Useful practices include scanning for weak signals (small, early indicators of larger shifts), writing two or three plausible scenarios for your market, and asking which roadmap bets survive in each. Treat the roadmap as a portfolio: protect the core, extend into adjacent needs, and fund a few small exploratory bets with clear kill criteria.
Employers will expect graduate product managers to connect customer insight with business model thinking, not just write user stories. Be ready to explain how a product would be discovered, evaluated and used by an AI agent, what that does to pricing and retention, and which capabilities remain defensible. Candidates who can reason about value capture, not only usability, stand out.
Choose a software product you use weekly. Write a one-page memo describing how an AI agent would use it on your behalf, what would break, and what the company should change in its interface, pricing and data strategy. Finish with two scenarios for the product in three years and the first experiment you would run to test which one is emerging.
Product Management · question 2 of 12
Topic 02 · The Future of Money & Fintech
Payments used to be something a product handed off to a bank or card network at checkout. That boundary is dissolving. Real-time payment systems such as FedNow in the US, UPI in India and Pix in Brazil settle in seconds, around the clock. Stablecoins, digital tokens pegged to currencies like the dollar, gained a US federal framework through the GENIUS Act in 2025. Embedded finance lets non-financial products offer payments, credit or insurance inside their own experience.
When moving money is instant and cheap, pricing models that once made little sense become practical. Very small payments, pay-per-use, pay-as-you-go subscriptions and instant refunds can all be designed into the experience. Product managers will increasingly treat pricing and billing as parts of the product to design and test, not as settings finance configures after launch.
Every extra step in a payment flow loses customers. Real-time rails, stored credentials and wallet payments can shorten checkout dramatically, but each market has its own dominant methods. A product launching in India, Brazil and the US may need three quite different payment experiences. Understanding local payment habits is now a core part of international product strategy.
Platforms that already hold rich data about their users, such as marketplaces, software for small businesses or gig-work apps, can offer financial services at the moment of need: instant payouts for sellers, short-term credit for inventory, or insurance tied to a booking. These services can deepen loyalty and add revenue, but they bring regulatory obligations, partner dependencies and fraud risks that product managers must understand before committing.
Money features fail loudly. A confusing refund flow or an unexpected charge damages trust faster than almost any other bug. Product managers working near payments must partner closely with compliance, legal and risk teams, design clear consent and dispute flows, and plan for fraud from day one. Instant settlement also means mistakes can be harder to reverse, which raises the bar for testing and safeguards.
Graduates who can speak confidently about payment flows, pricing experiments and embedded finance are valuable well beyond fintech companies, because nearly every digital product now touches money. Learn the basic vocabulary (authorization, settlement, chargeback, know-your-customer checks) and be ready to discuss how payment design affects conversion, retention and revenue.
Pick a product that currently charges a flat monthly subscription. Design an alternative pricing model enabled by real-time or embedded payments, such as usage-based billing or instant payouts to its users. Sketch the new checkout or billing flow in five screens, list the top three risks, and define the metric you would use to judge whether the change succeeded in a pilot.
Product Management · question 3 of 12
Topic 03 · Reinventing Work for AI
A large share of a product manager’s traditional output, such as requirement documents, competitive summaries, meeting notes, survey analysis and even clickable prototypes, can now be drafted by AI in minutes. That does not remove the role. It changes where the value lies: less in producing artifacts, more in judgment, prioritization, alignment and knowing which problems are worth solving.
When anyone on the team can generate a polished specification, polish stops signaling quality. What matters is whether the underlying reasoning holds: is the customer problem real, is the evidence strong, are the trade-offs explicit? Product managers who can frame sharp questions, weigh imperfect evidence and make a clear call will be more valuable, not less.
AI coding assistants and prototyping tools let product managers and designers build working prototypes themselves and put them in front of customers within days. This shortens the loop between idea and evidence. It also blurs traditional boundaries between product, design and engineering, so teams need clear agreements about who owns quality, security and long-term maintainability of anything that ships.
Product teams are starting to use AI agents for tasks like triaging customer feedback, drafting release notes and monitoring metrics. Structuring this well means deciding which tasks are fully automated, which need a human to review, and which should stay entirely human because they involve judgment, relationships or ethics. Treat these workflows like products: define the job, measure the quality and iterate.
AI outputs can be confidently wrong, and summaries of customer interviews can quietly drop the most surprising insight. Over-reliance can erode the deep customer understanding that makes great product managers. Teams also need rules on what data can be pasted into AI tools, especially customer data and unreleased plans. Good practice is to keep direct customer contact as a non-negotiable habit, however much analysis is automated, and to check AI summaries against the original source before acting on them.
Entry-level product roles will expect fluency with AI tools from the first week. The differentiator will be the skills AI does not supply: curiosity about customers, structured thinking, persuasive communication and ethical judgment. Show employers you can use AI to move faster while still doing the hard thinking yourself, and that you can explain where you would not trust it.
Take a product idea and use AI tools to produce a problem statement, three user personas, a short requirements document and a clickable prototype in one afternoon. Then critique the outputs: note where the AI was useful, where it was generic or wrong, and what you had to add from your own judgment. Bring that honest comparison to interviews as evidence of how you work.
Product Management · question 4 of 12
Topic 04 · The Future of Healthcare
Health is no longer confined to hospitals and medical devices. Fitness apps, smartwatches, sleep trackers, meditation tools, food delivery services and even workplace software now collect or infer information about people’s bodies and minds. At the same time, society is questioning the effect of attention-maximizing design on mental health. For product managers, health has become both an opportunity and a responsibility, even in products that are not health products.
Information about a person’s health is among the most sensitive data a product can hold. In the US, health privacy law covers some organizations but not all, and several states have added their own consumer health data rules. In Europe, health data is a special category under data protection law. Some AI uses in health are also classified as high risk under the EU AI Act. The practical lesson: collect only what you need, explain why clearly, and involve legal and privacy experts early.
There is a real line between general wellness features and software that diagnoses or treats a condition. Crossing it can trigger medical device regulation, clinical evidence requirements and longer timelines. Product managers must know where that line sits for their market before promising features such as early warnings or personalized health advice, because marketing language alone can change a product’s regulatory status.
Engagement metrics such as time spent and daily sessions can reward designs that harm users, like endless feeds or anxiety-inducing notifications. Responsible teams add counter-metrics, such as user-reported satisfaction or healthy usage patterns, and review features for potential harm to vulnerable groups, including young people. Accessibility, clear controls and easy ways to pause or leave all signal respect for the user.
Done well, health-aware products create real value: tools that help people manage chronic conditions, remind them about medication, support caregivers or make healthcare services easier to navigate. Employers are also buying digital wellbeing and benefits products for their workforce. These markets reward teams that combine strong design with rigor, evidence and trust.
Even if you never work at a health company, you may manage a product that touches health data or affects wellbeing. Employers value graduates who can spot these issues early, ask the right questions of legal and clinical colleagues, and propose metrics that balance growth with user welfare. That judgment protects both users and the company’s reputation.
Choose a popular consumer app outside healthcare. List every piece of data it collects or could infer that relates to health or wellbeing. Then propose one feature that would genuinely improve users’ wellbeing, the counter-metric you would track alongside engagement, and the three questions you would ask a privacy lawyer before building it.
Product Management · question 5 of 12
Topic 05 · Regenerative Enterprise & Ecosystem Innovation
Products have traditionally been designed for a linear path: make, sell, use, discard. Regulation, customer expectations and resource costs are pushing toward circular models, where products are designed to last longer, be repaired, reused or recycled. The EU, for example, is introducing digital product passports and ecodesign rules that require information about materials and repairability. Regenerative thinking goes further, asking how a business can restore the systems it depends on.
A product manager’s decisions shape most of a product’s environmental impact, often long before manufacturing begins. Choices about materials, modularity, packaging, software support periods and upgrade paths determine whether a product lasts two years or ten. Life cycle assessment (a structured estimate of environmental impact from raw materials to disposal) gives teams a shared picture of where the biggest impacts actually sit. It often reveals surprises, for example that packaging, shipping or the energy a product uses during its life outweighs the materials it is made from.
Software has an environmental footprint through the data centers and devices that run it. AI features in particular can be energy-intensive, and the IEA projects that data center electricity use could roughly double by 2030. Product managers can reduce impact by choosing efficient models, avoiding unnecessary computation, and extending support for older devices so customers are not pushed into early upgrades.
Circularity is easier when the business earns more from durability. Product-as-a-service models (leasing, subscriptions with maintenance included), refurbishment programs, spare-parts sales and trade-in schemes align profit with longer product life. The product manager’s task is to test whether customers will adopt these models and whether the unit economics hold once returns, repairs and logistics are included.
No product team closes the loop alone. Circular products depend on suppliers who can provide recycled or traceable materials, repair networks, logistics partners for returns, recyclers who can recover materials, and sometimes competitors cooperating on shared standards. Product managers increasingly coordinate across these partners, which makes negotiation, systems thinking and clear data sharing core skills.
Sustainability is moving from a communications topic to a product requirement with regulatory weight. Graduates who can bring life cycle thinking into product decisions, credibly and without exaggeration, will be valuable in consumer goods, electronics, software and services. Avoid unsupported environmental claims; regulators are increasingly acting against misleading green marketing.
Pick a product you own, physical or digital. Map its life cycle from materials to disposal, mark the two stages where you believe impact is highest, and propose one design change and one business model change that would extend its useful life. Identify the ecosystem partner each change would require and the metric you would use to prove it works.
Product Management · question 6 of 12
Topic 06 · Robotics & Physical AI
AI is moving off the screen and into the physical world. Warehouse robots, delivery robots, robotic vacuum cleaners, drones and early humanoid robots combine sensors, motors and AI models that interpret their surroundings. This category, often called physical AI, is growing as hardware costs fall and AI models become better at understanding images and space. It brings product management challenges that software alone never had to face.
Software can be updated daily; hardware decisions are often locked in months or years before launch. Product managers for physical products must make early bets on sensors, batteries, materials and cost targets, knowing mistakes are expensive to fix. Many teams now blend the two by shipping capable hardware and improving behavior through software updates, which requires careful planning of what can and cannot change after launch.
A software bug might show the wrong screen; a robot bug can injure someone or damage property. Physical AI products need rigorous safety analysis, testing in realistic environments, clear limits on where and how the device operates, and dependable ways to stop it. Standards bodies and regulators are active in this area, and product managers must treat compliance as part of the core roadmap.
People need to understand what a robot is about to do. Clear signals (lights, sounds, movement patterns), simple ways to intervene and honest communication about limitations build trust. In workplaces, adoption depends heavily on the workers who share space with the robot, so involving them early in design and pilots is both ethical and practical. Their feedback often reveals everyday problems, such as blocked aisles or confusing alerts, that lab tests miss.
Many physical AI products are sold as robots-as-a-service, where customers pay a monthly fee that includes maintenance and updates. This lowers the upfront barrier but makes reliability and support costs central to profitability. Robots also collect large amounts of data about spaces and people, raising privacy questions that product managers must resolve before customers raise them.
Physical AI is a growing field for product managers who can bridge engineering, operations and customer needs. Employers value graduates who understand hardware trade-offs, respect safety processes and can run field pilots that generate real evidence. Even a basic grasp of how sensors, batteries and AI models interact will set you apart from purely software-focused candidates.
Pick a task in a warehouse, hospital, hotel or home that a robot could plausibly take on. Write a one-page product brief covering the user, the job to be done, the operating environment, the top three safety risks and how the robot would signal its intentions to people nearby. Define the pilot you would run and the evidence that would justify scaling.
Product Management · question 7 of 12
Topic 07 · The Energy Transition
Energy used to be invisible to most product teams: a utility cost handled by operations. That is changing. AI features require substantial computing power, and the IEA projects that data center electricity use could roughly double by 2030. Meanwhile, homes, vehicles and businesses are electrifying, and grids are adding large amounts of variable solar and wind power. Energy is becoming a design constraint and a market opportunity at the same time.
Every AI response has a real computing and energy cost, unlike traditional software where serving one more user costs almost nothing. Product managers must weigh which features truly need a large model, where a smaller or on-device model would do, and how often results can be reused rather than regenerated. These choices affect margins, response speed and environmental impact together. A feature that looks cheap in a demo can become a significant cost line once millions of customers use it every day.
As grids rely more on sun and wind, the value of electricity increasingly depends on when it is used. Products that can shift demand in time, such as electric vehicle chargers, heat pumps, batteries and smart thermostats, can save users money and help grid operators. The product challenge is making this flexibility automatic and trustworthy, so users benefit without having to think about tariffs.
The energy transition is creating demand for software and services that did not exist a decade ago: home energy management apps, fleet charging platforms, tools for tracking and reporting emissions, and marketplaces for rooftop solar and storage. Many of these products sit between utilities, device makers and customers, so understanding each party’s incentives is central to getting adoption.
Heat waves, storms and grid stress make reliability a growing concern. Products that keep working during outages, degrade gracefully when power or connectivity is limited, or help customers prepare for disruptions can differentiate on trust. For hardware products, battery life and power efficiency increasingly shape customer satisfaction and reviews.
Product managers who understand the basics of energy, such as the difference between capacity and consumption, time-of-use pricing and the costs of running AI, can make better trade-offs and speak credibly with engineering and finance. Energy literacy is also a strong entry point into the fast-growing climate technology sector, where product talent is in demand.
Choose an AI feature in a product you use. Estimate, qualitatively, how often it runs and whether every call genuinely needs a large model. Propose two changes that would reduce its computing cost without hurting the user experience. Alternatively, sketch a simple product that helps a household shift electricity use to cheaper hours, and define its core success metric.
Product Management · question 8 of 12
Topic 08 · Blockchain & Business Applications
Blockchain has moved through cycles of hype and disappointment. What remains are practical uses where several parties who do not fully trust each other need a shared, tamper-resistant record. Stablecoins are becoming part of mainstream payments, supported in the US by the GENIUS Act in 2025, and companies are testing tokenized assets, supply chain traceability and digital credentials. For product managers, the key skill is telling real use cases from technology looking for a problem.
A distributed ledger (a shared record copied across many computers, which no single party controls) is slower and more complex than a normal database. It earns its place only when several conditions hold: multiple organizations need to write to the same record, they do not want one party in control, and the record must be verifiable later. If one trusted company can run the database, it usually should.
Promising applications include proving where a product came from (useful for food, pharmaceuticals and luxury goods), portable digital credentials such as certificates that a user owns and can share, faster cross-border payments using stablecoins, and digital product passports that follow an item through repairs and resale. In each case the customer benefit is trust or portability, not the technology itself.
Early blockchain products often asked users to manage cryptographic keys, pay unpredictable fees and understand unfamiliar terms. Mainstream adoption requires the opposite: familiar sign-in, clear pricing, recovery options if a device is lost, and language that describes benefits, not mechanisms. Good product managers make the ledger invisible unless showing it adds trust.
Tokens can trigger financial regulation, and rules differ by country. Records on a public ledger may be permanent, which can conflict with privacy laws that give people the right to have data deleted. Security failures in this space have been costly. Product managers need early involvement from legal, security and compliance teams, and a clear plan for what happens when something goes wrong, including how customers will be supported and compensated.
You do not need to be a cryptography expert, but employers value product managers who can evaluate blockchain proposals calmly, ask the right-tool questions and design experiences ordinary users can trust. That skepticism, paired with openness to genuine use cases, is exactly what leadership teams look for when budgets are on the line.
Find a publicly announced blockchain project in an industry you care about. Apply the right-tool test: who writes to the record, who would otherwise control it, and what customers gain. Write a short verdict on whether a conventional database would have worked, and describe the one customer experience change you would make to improve adoption.
Product Management · question 9 of 12
Topic 09 · Designing Spatial Computing Experiences
Spatial computing blends digital content with the physical world. Augmented reality overlays information on what you see; mixed and virtual reality create immersive environments. Headsets, smart glasses and phones with depth sensors are improving, and AI makes it easier to understand scenes and generate 3D content. Consumer adoption of headsets has been slower than early forecasts, but industrial, training and retail uses are showing steady value.
The strongest spatial products solve problems that are genuinely spatial: guiding a technician through a repair on the actual machine, letting a customer see furniture in their own room, training staff for rare or dangerous situations, or helping teams review a physical design together. If the task works fine on a flat screen, a headset rarely improves it. The product manager’s first job is to be honest about that.
Spatial interfaces respond to gaze, hand gestures, voice and body movement, not just taps. Content must stay readable at different distances and lighting, respect the user’s physical safety, and avoid causing discomfort or motion sickness. Sessions tend to be shorter, and onboarding must teach new interactions quickly. Prototyping in the actual environment, not only on a desk, is essential, because real lighting, noise, clutter and interruptions change how people behave and what they can comfortably see.
Standard metrics such as clicks and screen views translate poorly. Spatial products are better judged by task outcomes: time to complete a repair, errors avoided, training retention, returns reduced after a virtual try-on. Comfort and session length also matter. Defining these outcome metrics early helps secure support from business stakeholders who may be skeptical of the technology.
Spatial devices use cameras and sensors that capture people’s homes, workplaces and bystanders, and can record eye movements that reveal attention. Product managers must decide what is processed on the device, what is stored and what is shared. Accessibility is also critical: users with limited mobility, vision differences or motion sensitivity need alternatives.
Spatial computing is still early, which gives graduates a chance to build distinctive expertise. Employers value product managers who can identify where immersive experiences create measurable value, run focused pilots and communicate results clearly. Familiarity with basic 3D design concepts and hands-on time with current devices will make your judgment more credible.
Choose a task in an industry you know that involves physical space, such as assembling, inspecting, shopping or training. Write a short product brief comparing a flat-screen solution with a spatial one. State which you would build and why, the outcome metric you would use in a pilot, and two design principles to keep users comfortable and safe.
Product Management · question 10 of 12
Topic 10 · Agility in the Age of AI
Agile methods helped software teams move from yearly releases to frequent iterations. AI is compressing cycles again. Code, designs, tests and analysis can be produced far faster, so competitors can copy features quickly and markets can shift within a quarter. Speed of shipping alone is no longer a sustainable advantage. The new edge is speed of learning: how quickly a team turns evidence into better decisions.
Many teams still measure progress by features delivered. In an AI-accelerated world, that metric can reward busy activity rather than value. Stronger teams define outcomes, such as a change in customer behavior or a business result, and treat features as experiments that may or may not move them. Product managers set clear hypotheses, success thresholds and decision dates before building.
Fast teams keep regular, ongoing contact with customers rather than doing research only at the start of a project. Weekly interviews, lightweight prototypes and small live tests keep the team grounded. AI can help by summarizing feedback and spotting patterns, but product managers should still hear customers directly, because surprising details are what drive real insight.
Speed fails when every choice needs senior approval. Teams move faster when they know which decisions they can make themselves and which require escalation. Clear guardrails, such as security standards, brand principles and responsible AI rules, allow teams to act quickly within safe limits. Product managers help define these boundaries, communicate them widely and revisit them as the team learns. Short written decision records, noting what was decided, why and on what evidence, help new team members understand past choices and prevent the same debates from repeating.
Faster building makes it tempting to keep everything. Mature teams remove features that are not used, stop experiments that fail their thresholds, and resist letting the product become cluttered. Sunsetting work is a sign of disciplined learning, not failure. It also frees engineering capacity and reduces the long-term cost of maintaining code nobody needs.
Graduates who can write a testable hypothesis, design a small experiment and read the results honestly will contribute quickly. Employers look for evidence that you learn from data rather than defending your first idea. Being comfortable with ambiguity, and able to explain your reasoning clearly to engineers and executives, is central to the role in AI-accelerated organizations.
Take a feature idea for a product you use. Write a one-page experiment plan: the customer problem, your hypothesis, the smallest test that could disprove it, the metric and threshold for success, and the decision you will make in each outcome. Run a lightweight version, even a survey or clickable prototype shown to five people, and summarize what you learned.
Product Management · question 11 of 12
Topic 11 · The Future of Mobility
Mobility is changing on several fronts at once. Robotaxis now operate commercially in several US and Chinese cities. Electric bikes and scooters are common in many urban areas. Delivery is becoming faster and more automated, with sidewalk robots and drones in trial or limited service. Vehicles themselves are becoming software platforms, updated over the air. Each change alters where customers are, how much time they have and how products reach them.
When people no longer need to drive, travel time becomes available for work, entertainment, shopping or rest. In-car screens, voice assistants and connected services are becoming a new channel. Product managers in media, retail and productivity software will increasingly ask how their product fits a twenty-minute ride, and what the platform owner will allow.
Customers who are used to same-day or same-hour delivery apply that expectation everywhere. Products that involve physical goods must design around real-time tracking, flexible delivery windows, easy returns and clear communication when something slips. Automated delivery adds new questions: how does a customer receive a package from a robot, and what happens if nobody is home?
Mobility data, such as where people travel and when, enables helpful services: suggesting a pickup point, adjusting store stock to commuter patterns or timing offers to arrival. It is also highly sensitive, because movement patterns can reveal where someone lives, works and worships. Product managers should collect location data sparingly, explain its use plainly and give users meaningful control.
New mobility can widen or narrow access. Services that assume a smartphone, a bank card or a dense city may exclude older people, rural customers or people with disabilities. Thoughtful product design includes alternatives, such as phone booking, accessible vehicles and cash-friendly options, and considers the effect on people who share streets with new vehicles. Testing with diverse users, not only early adopters in large cities, reveals barriers that internal teams often overlook and helps a product reach a much larger market.
Whatever sector you join, mobility shifts will affect your customers’ routines and your product’s logistics. Graduates who can map how customers move and connect that to product and channel decisions bring a broader view than peers focused only on screens. Mobility companies themselves are also hiring product managers who understand operations, safety and regulation.
Choose a product or service you use regularly. Map a typical customer’s journey today, then redraw it assuming robotaxis and automated delivery are common in their city. Identify two new moments where the product could add value, one risk to privacy or access, and the first feature you would prototype to test customer interest.
Product Management · question 12 of 12
Topic 12 · AI & Quantum
AI has moved from a specialist feature to a default expectation in digital products. At the same time, regulators are setting rules, most notably the EU AI Act, which classifies AI uses by risk and places stricter duties on high-risk systems. Quantum computing, which uses quantum physics to tackle certain problems classical computers struggle with, is still maturing but already affects security planning today.
Not every feature benefits from AI. Good candidates share traits: the task is repetitive or data-heavy, occasional errors are tolerable or easy to catch, and the value to users is clear. Poor candidates involve high-stakes decisions with little room for mistakes, or situations where users need to understand exactly why something happened. Product managers should ask what the AI adds, what happens when it is wrong and how users will know.
AI features need honest communication about what they can do, visible signals when content is generated, easy ways to correct or override results, and clear routes to a human when needed. Teams should test for bias, harmful outputs and misuse before launch, then monitor after release, because AI behavior can shift as data and usage change. Tracking user corrections and complaints gives early warning of problems.
Responsible AI is not only a legal task. Product managers can keep a simple record for each AI feature: its purpose, data sources, known limitations, risk level and owner. This makes regulatory compliance easier, speeds up reviews and helps teams respond quickly when problems arise. Under the EU AI Act, some product categories require formal documentation and human oversight.
Future large quantum computers are expected to break some encryption methods used widely today. Because attackers can steal encrypted data now and decrypt it later, sensitive data with a long life is already at risk. NIST published its first post-quantum cryptography standards in 2024. Product managers should help ensure their products can switch encryption methods without major rebuilds, and track which data needs protection longest.
Employers expect graduate product managers to discuss AI features with nuance: benefits, limitations, risks and regulation. Understanding quantum computing at a conceptual level, especially its security implications, marks you as someone who thinks ahead. Both topics reward clear explanation for non-specialist colleagues, a core product management skill.
Pick an AI feature in a product you use. Write a one-page review covering its purpose, likely data sources, what happens when it is wrong, how users are informed and where it might fall under the EU AI Act’s risk categories. Add a paragraph on which of the product’s data would still need protecting in ten years and why that matters for quantum readiness. FIELD 3 OF 26 · MANUFACTURING & OPERATIONS
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| Leaders | Per leader | Example total |
|---|---|---|
| 1–24 | US$1,045 | – |
| 25–99 | US$9955% off | 25 = US$24,875 |
| 100–199 | US$89015% off | 100 = US$89,000 |
| 200+ | US$83520% off | 200 = US$167,000 |